title: Evaluation of prognostic clinical and ECG parameters in patients after myocardial infarction by applying logistic regression method.
abstract: Noninvasive risk stratification of patients who have suffered myocardial infarction (MI) is one of the greatest challenges in today's cardiology. No single test has sufficient predictive ability. Therefore, a combination of the tests must be applied for better post-MI risk stratification. The purpose of this study was to assess noninvasive predictors of 2 years cardiac mortality in post-MI patients and create a stratification model for identification of high-risk patients. Clinical, electrocardiographic, and echocardiographic parameters were evaluated before hospital discharge in 180 survivors of acute MI (mean age 57.0 +/- 9.9, male 82.2%), followed up for 2 years. A multivariate logistic regression analysis was used to determine informative predictors of cardiac mortality. A clinical score was constructed using the regression coefficient from the multivariate model. During follow-up, 16 deaths (8.8%) occurred. Multivariate analysis identified a combination of six variables that showed the strongest association with cardiac mortality. Based on the coefficients of the logistic regression, six variables were used to create a scoring system: filtered QRS duration (QRSd) >114 ms, coefficient of variation (Cv) <or=2.5%, maximal corrected QT interval (QTcmax) >or=445 ms, left ventricular ejection fraction (LVEF) <or=30%, the absence of use of beta-blockers (BB), and absence of treatment by primary percutaneous coronary intervention (PCI). The cutoff value for the score of 10 or higher, defined the "high-risk" patients with a sensitivity of 75% and a specificity of 70%. The mortality rate for the low- and high-risk groups were 12.5% and 87.5%, respectively. The receiver operating characteristic (ROC) analysis yielded area under curve of 0.88. The proposed scoring system could be valuable in predicting 2 years risk of cardiac mortality in post-MI population and allows the stratification of patients into low- and high-risk groups.
authors.affiliation: Kaunas University of Medicine, Institute of Cardiology, Kaunas, Lithuania. urbograz@gmail.com
authors.forename: Grazina
authors.initials: G
authors.lastname: Urbonaviciene
authors.forename: Sigitas
authors.initials: S
authors.lastname: Urbonavicius
authors.forename: Henrik
authors.initials: H
authors.lastname: Vorum
authors.forename: Ina
authors.initials: I
authors.lastname: Bluzaite
authors.forename: Gediminas
authors.initials: G
authors.lastname: Jarusevicius
authors.forename: Bent
authors.initials: B
authors.lastname: Honoré
authors.forename: Minija
authors.initials: M
authors.lastname: Tamosiunaite
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006801:Humans; D015994:Incidence; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D009203:Myocardial Infarction / Q000175:diagnosis* / Q000401:mortality*; D011379:Prognosis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors; D012680:Sensitivity and Specificity; D016019:Survival Analysis; D015996:Survival Rate